def print_results(self):
# Print every players top 10 most common words
for speaker, count in self.speakers.items():
wordFreqPairs = count.most_common(10)
table = generate_table(wordFreqPairs)
print(f"\n{speaker}:")
print(table)
totalWords = PrettyTable()
totalWords.field_names = ["Words", "Player"]
totalWords.sortby = "Words"
totalWords.reversesort = True
for player, count in self.totalWords.items():
totalWords.add_row([count, player])
print(f"\nTotal Words by Player")
print(totalWords)
table = generate_table(self.allSpeakers.most_common(20))
print(f"\nMost Common Words:")
print(table)
def create_layout(app):
return html.Div(
[
Header(app),
# page 2
html.Div(
[
# Row
html.Div(
[
html.Div(
[
html.H6(["Team Location"],
className="subtitle padded"),
# scatter geo plot of nba teams' location
dcc.Graph(
id='scatter_geo',
className='my_graph',
figure={
'data': [{
# Setting coordinate and description for each team
'lon': [
-98.37, -97.52, -87.90,
-75.16, -118.24, -122.27,
-79.38, -112.07, -90.07,
-122.675, -80.84, -77.04,
-83.05, -93.265, -71,
-111.89, -87.63
],
'lat': [
29.76, 35.47, 43, 39.95,
34.05, 37.8, 43.65, 33.45,
29.95, 45.5, 35.23, 38.91,
42.33, 44.98, 42.37, 40.76,
41.88
],
'text': [
'HOU, Houston Rocket',
'OKC, Oklahoma Thunder',
'MIL, Milwaukee Bucks,',
'PHI, Philadelphia 76ers',
'LAL, Los Angeles Lakers',
'GSW, Golden State Warriors',
'TOR, Toronto Raptors',
'PHX, Phoenix Suns',
'NOP, New Orleans Pelicans',
'POR, Portland Trail Blazers',
'CHA, Charlotte Hornets',
'WAS, Washington Wizards',
'DET, Detroit Pistons',
'MIN, Minnesota Timberwolves',
'BOS, Boston Celtics',
'UTA, Utah Jazz',
'CHI, Chicago Bulls'
],
'type':
'scattergeo',
'mode':
'markers',
"marker": {
"size": 10,
"opacity": 10.0,
},
}],
'layout': {
'title': 'NBA teams location',
'height': 600,
'yaxis': {
'hoverformat': '.2%'
},
'margin': {
'l': 35,
'r': 35,
't': 50,
'b': 80
},
"geo": {
"scope": "usa",
'showframe': True,
'showcoastlines': True,
},
"colorbar": True,
}
},
config={'displayModeBar': True})
],
className="six columns",
),
html.Div(
[
html.H6(
["Salary Distribution"],
className="subtitle padded",
),
# Scatter plot of player's salaries
dcc.Graph(
id='graph-scatter',
className='my_graph',
figure={
'data': [{
# Name of each player on x axis and their corresponding salary on y axis
'x': df['Name'],
'y': df['Salary'],
'type': 'scatter',
'mode': 'markers',
}],
# Description of the plot
'layout': {
'title':
'Distribution of NBA players salaries',
'height': 600,
'yaxis': {
'hoverformat': '.2%'
},
'margin': {
'l': 35,
'r': 35,
't': 50,
'b': 80
},
}
},
config={'displayModeBar': False})
],
className="six columns",
),
],
className="row ",
),
# Row 2
html.Div(
[
html.Div(
[
html.H6("LeBron James choose Lakers",
className="subtitle padded"),
# Plot image from web for a overall sleek design
html.Img(
src=
'https://i.pinimg.com/originals/9f/15/98/9f15989577e13555c75031ee72d9c9a5.jpg',
style={
'height': '100%',
'width': '100%',
'float': 'right',
'position': 'relative',
'padding-top': 0,
'padding-right': 0
})
],
className="twelve columns",
)
],
className="row ",
),
# Row 3
html.Div(
[
html.Div(
[
html.H6(
["plot4"],
className="subtitle padded",
),
html.Div(
[generate_table(df)],
style={"overflow-x": "auto"},
),
],
className="twelve columns",
)
],
className="row ",
),
#end
],
className="sub_page",
),
],
className="page",
)
def show_table(input_value):
if input_value:
return generate_table(query_db(get_query(input_value)))
def quick_info_update(sym):
'''Updates tables on dashboards.
Section 4 requirement queries.
args:
sym: Stock symbol for "any" argument in requirements.
returns:
list: HTML headers and DCC tables
'''
# Really lazy hack to prevent SQL injection
valid_syms = [val for val, _ in GetStockSymbols()]
if sym not in valid_syms:
return "ERROR: Invalid symbol selection"
# Query 4.1 in requirements
qh1 = html.H3("Company Prices")
header = 'Price'
df = pd.read_sql(
'''
SELECT stock_price_minute.sym AS "Company", close AS "{}"
FROM stock_price_minute
JOIN (
SELECT sym, MAX(dateid) as mdate
FROM stock_price_minute
GROUP BY sym
) as sub
ON stock_price_minute.dateid = sub.mdate AND stock_price_minute.sym = sub.sym
ORDER BY dateid DESC, stock_price_minute.sym ASC
'''.format(header), db.engine)
df[header] = df[header].apply(money_format)
sym_table = generate_table(df, 10)
# Query 4.2 in requirements
qh2 = html.H3("Highest Price in 10 Days")
header = 'Highest Price'
df = pd.read_sql(
'''
SELECT sym AS "Company", MAX(close) AS "{}"
FROM stock_price_minute
WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
AND sym = '{}';
'''.format(header, sym), db.engine)
df[header] = df[header].apply(money_format)
max_table = generate_table(df, 10)
# Query 4.3 in requirements
qh3 = html.H3("Year Average Price")
header = 'Average Price'
df = pd.read_sql(
'''
SELECT sym AS "Company", AVG(close) AS "{}"
FROM stock_price_minute
WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
AND sym = '{}';
'''.format(header, sym), db.engine)
df[header] = df[header].apply(money_format)
avg_table = generate_table(df, 1)
# Query 4.4 in requirements
qh4 = html.H3("Year Lowest Price")
header = 'Lowest Price'
df = pd.read_sql(
'''
SELECT sym AS "Company", MIN(close) AS "{}"
FROM stock_price_minute
WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
AND sym = '{}';
'''.format(header, sym), db.engine)
df[header] = df[header].apply(money_format)
low_table = generate_table(df, 1)
# Query 4.5 in requirements
qh5 = html.H3("Average Less Than Lowest of {}".format(sym.upper()))
header = 'Average Price'
df = pd.read_sql(
'''
SELECT sym AS "Company", sub1.close AS "{}"
FROM (
SELECT sym, AVG(close) AS "close"
FROM stock_price_minute
WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
GROUP BY sym
) as sub1
LEFT JOIN (
SELECT MIN(close) AS criteria
FROM stock_price_minute
WHERE dateid >= DATE_SUB(CURDATE(), INTERVAL 365 DAY)
AND sym = '{}'
) AS sub2
ON 1 = 1
GROUP BY sym, criteria
HAVING sub1.close < criteria
ORDER BY sym ASC;'''.format(header, sym), db.engine)
df[header] = df[header].apply(money_format)
avglow_table = generate_table(df, 100)
if len(dash_data['y_preds']['_all']) <= 0:
display_val = 'Loading...'
else:
display_val = ' ${0:.2f}'.format(dash_data['y_preds']['_all'][-1])
display_val = ' ${0:.2f}'.format(dash_data['y_preds']['_all'][-1])
# Buy sell summary
pred_list = [html.H3("Predicted Value")]
if len(dash_data['y_preds']['_all']) <= 0:
pred_list.append(
html.Img(src=load_img_url, style={'height': '50px'}))
elif dash_data['y_preds']['_all'][-1] > dash_data['closes'][-1]:
pred_list.append(
html.Img(src=buy_img_url, style={'height': '50px'}))
else:
pred_list.append(
html.Img(src=sell_img_url, style={'height': '50px'}))
pred_list.append(display_val)
pred_data = html.Div(pred_list)
return [
pred_data, qh1, sym_table, qh2, max_table, qh3, avg_table, qh4,
low_table, qh5, avglow_table
]
# 'title': 'Comments are clustered into 10 topics',
# 'xaxis':{'title':'x-axis label'},
'yaxis': {
'title': 'Number of comments per topic'
},
}
},
),
],
className="six columns"),
# View individual comments
html.Div([
html.H6('Top 5 words in each topic:'),
html.Br(),
generate_table(df.drop(['Comments'], axis=1))
],
className="six columns"),
],
className="Row"),
# Pick a selected comment to view highlighting
html.Div([
html.H6('Input a comment number to view highlighting by topic:'),
dcc.Input(id='number-in', value=1, style={'fontSize': 20}),
html.Button(id='submit-button',
n_clicks=0,
children='Submit',
style={'fontSize': 20}),
html.H4('Highlighting indicates topic area:'),
html.Iframe(
import utils import numpy as np import matplotlib.pyplot as plt country = 'France' sol1 = "Data/sol_jan (2).xls" sol2 = "Data/sol_feb (2).xls" sol3 = "Data/sol_march (2).xls" win1 = "Data/win_jan (2).xls" win2 = "Data/win_feb (2).xls" win3 = "Data/win_march (2).xls" price1 = "Data/price_jan.xlsx" price2 = "Data/price_feb.xlsx" price3 = "Data/price_march.xlsx" #t = read_generated_energy(sol1,win1); tab1 = utils.generate_table(sol1, win1, price1, country) tab2 = utils.generate_table(sol2, win2, price2, country) tab3 = utils.generate_table(sol3, win3, price3, country) tab = tab1 + tab2 + tab3 sol, win, price = utils.sort_by_hour(tab) plt.hist(sol[10]) #histogramme de la production solaire à 10h
figure={
'data': get_data_of_count_words(),
'layout': {
'title': 'Dash Data Visualization'
}
}),
],
className="six columns",
style={
'width': '49%',
'display': 'inline-block',
'vertical-align': 'middle'
}),
],
className="row"),
html.Div([generate_table(user_df)])
])
@app.callback(dash.dependencies.Output('user-indicator-scatter', 'figure'), [
dash.dependencies.Input('user-dropdown', 'value'),
])
def update_graph(owner_id):
print('owner_id %s' % str(owner_id))
owner_id = int(owner_id)
user_df_updated = user_df[user_df['ownerId'] == owner_id]
return {
'data': [
go.Scatter(